Methods and apparatus to perform multi-focal plane image acquisition and compression

ABSTRACT

Example methods and apparatus to perform multi-focal plane image acquisition and compression are disclosed. A disclosed example method includes capturing a first image of a portion of an object at a first focal plane and at a first resolution, computing a contrast metric for the captured first image, comparing the contrast metric to a threshold to determine whether to capture a second image of the portion of the object at the first focal plane and at a second resolution, wherein the second resolution is different from the first resolution, capturing the second image of the portion of the object at the first focal plane and at the second resolution, and storing a first representation of the second image in a file, the file containing a second representation of the portion of the object at a second focal plane, wherein the second representation is at the first resolution.

RELATED APPLICATION

This patent arises from a continuation of U.S. application Ser. No.12/410,146, filed Mar. 24, 2009, which is hereby incorporated herein inits entirety.

FIELD OF THE DISCLOSURE

This disclosure relates generally to image acquisition and, moreparticularly, to methods and apparatus to perform multi-focal planeimage acquisition and compression.

BACKGROUND

In the emerging field of digital pathology, images of tissue sampleslides are digitally scanned and/or imaged, and saved as digital images.Each such image may consume as much as 10-to-25 Gigabytes (GB) ofstorage. The tissue sample, which is placed on the slide, may have athickness of a few microns to a few millimeters. In some examples, theslides are scanned into a single two-dimensional (2D) image, where anautofocus algorithm determines and/or selects a focal plane for eacharea, region and/or portion of the image. In other examples, the slideis completely imaged and/or captured for and/or on each of a number offocal planes. Accordingly, a file representing the slide contains aplurality of 2D images for respective ones of a plurality of focalplanes. The various 2D focal plane images can then be interpolated toallow a pathologist to interactively review any portion(s) of thedigital slide at different focal planes.

BRIEF DESCRIPTION OF THE INVENTION

Example methods and apparatus to perform multi-focal plane imageacquisition and compression are disclosed. In general, the examplesdisclosed herein adaptively scan and/or image a slide to substantiallyreduce storage requirements while maintaining sufficient resolution anda sufficient number of focal planes to permit analysis of the slide by apathologist. When a slide is scanned at a particular focal plane,different regions, areas and/or portions of the slide may be adaptivelycaptured at different resolutions depending on whether particular regionexhibits one or more characteristics representative of potential and/oranticipated interest by a pathologist. Additional savings may berealized by the application of multi-scale wavelet transforms.

A disclosed example method includes capturing a first image of a portionof an object at a first focal plane and at a first resolution, computinga contrast metric for the captured first image, comparing the contrastmetric to a threshold to determine whether to capture a second image ofthe portion of the object at the first focal plane and at a secondresolution, wherein the second resolution is different from the firstresolution, capturing the second image of the portion of the object atthe first focal plane and at the second resolution, and storing a firstrepresentation of the second image in a file, the file containing asecond representation of the portion of the object at a second focalplane, wherein the second representation is at the first resolution.

A disclosed example apparatus includes a contrast detector to compute acontrast metric for a first image of an object at a first focal planeand at a first resolution, an acquisition controller to compare thecontrast metric to a threshold to determine whether to capture a secondimage of the object at the first focal plane and at a second resolution,wherein the second resolution is different from the first resolution, animage acquirer selectively operable to capture the first and secondimages, and an image compression module to store a first representationof the object at the first focal in a file, the file containing a secondrepresentation of the object at a second focal plane, wherein the secondrepresentation corresponds to a different resolution than the firstrepresentation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of an example adaptive multi-focalplane image acquisition and compression apparatus.

FIG. 2 illustrates an example method of imaging based on overlappingstrips and tiles.

FIGS. 3 and 4 illustrate example tissue samples having focal planes ofinterest that vary along an axis of an object.

FIG. 5 illustrates an example multi-resolution representation of animage.

FIG. 6 illustrates an example data structure that may be used toimplement the example compressed image of FIG. 1.

FIG. 7 illustrates an example virtual multi-resolution wavelet transformrepresentation of an object.

FIGS. 8 and 9 are flowcharts representative of example processes thatmay be carried out to implement the example adaptive multi-focal planeimage acquisition and compression apparatus of FIG. 1.

FIG. 10 is a schematic illustration of an example processor platformthat may be used and/or programmed to carry out the example process ofFIGS. 8 and 9, and/or to implement any or all of the example methods andapparatus described herein.

DETAILED DESCRIPTION

Some of the existing techniques that collapse multiple focal planes intoa single 2D image do not permit a pathologist to manually control thefocal plane for different regions of interest. Accordingly, such filesare generally unacceptable to pathologists. While other existingtechniques overcome this deficiency by capturing multiple complete 2Dimages at multiple focal planes, they require the storage of largeamounts of data. For example, a file containing ten 2D images for tendifferent focal planes might consume 100-to-250 GB of storage. If apatient has 10-to-30 such slides, as much as 7.5 Terabytes (TB) ofstorage may be required.

Example methods and apparatus to perform multi-focal plane imageacquisition and compression are disclosed that overcome at least thesedeficiencies. In general, the examples disclosed herein adaptively scanand/or image a slide to substantially reduce the acquisition time and/orthe amount of storage capacity required to represent the slide withsufficient resolution and at a sufficient number of focal planes topermit analysis of the slide via the digitally captured images by apathologist. For each region, area and/or portion of a slide at aparticular focal plane, the examples disclosed herein adaptivelydetermine at what resolution the portion of the slide is to be captured.A particular region exhibiting one or more characteristicsrepresentative of potential and/or anticipated interest by a pathologistare scanned at high(er) resolution, while other regions are scanned at alow(er) resolution. Thus, the image of a slide at a particular focalplane has regions that are scanned, captured and/or imaged at differentresolutions. Additional savings may be realized by the application ofmulti-scale wavelet transforms that permit compression of the differentresolution portions. Using adaptive scanning and wavelet transforms theamount of data needed to represent a particular focal plane may bereduced by as much as a factor of ten. Moreover, because some focalplanes may contain no regions of interest, they may consume even lessstorage capacity.

FIG. 1 is a schematic illustration of an example adaptive multi-focalplane image acquisition and compression apparatus 100 constructed inaccordance with the teachings of this disclosure. The example adaptivemulti-focal plane image acquisition and compression apparatus 100 ofFIG. 1 captures and/or acquires a multi-focal plane, multi-resolutionimage and/or representation of an object 105. While the example object105 of FIG. 1 includes a tissue sample on a slide, an image and/orrepresentation of any number and/or type(s) of medical and/ornon-medical objects 105 may be captured and/or acquired by the exampleapparatus 100.

To acquire, capture and/or otherwise obtain an image 107 of the object105, the example adaptive multi-focal plane image acquisition andcompression apparatus 100 of FIG. 1 includes an image acquirer 110 and afocal plane selector 115. The example image acquirer 110 of FIG. 1 maybe any number and/or type(s) of image capture device(s) capable toacquire, capture and/or otherwise obtain a digital image 107 thatrepresents all or a portion of the object 105. Example image acquirers110 include, but are not limited to, a digital camera and/or an imagesensor. The example image acquirer 110 is selectively configurableand/or operable to capture images 107 at different resolutions. Theexample focal plane selector 115 of FIG. 1 is selectively configurable,controllable and/or operable to adjust the focus of the image acquirer110 at a particular focal plane. An example focal plane selector 115includes, but is not limited to, a variable focus lens, and/or anynumber and/or type(s) of method(s) and/or algorithm(s) that may beapplied to a captured image to adjust and/or control an effective focalplane of the captured image.

To control the acquisition and/or capture of images, the exampleadaptive multi-focal plane image acquisition and compression apparatus100 of FIG. 1 includes an acquisition controller 120. The exampleacquisition controller 120 of FIG. 1 controls, configures and/oroperates the focal plane selector 115 via control signals and/or paths125 to focus the image acquirer 110 at a particular focal plane. Theexample acquisition controller 120 controls, configures and/or operatesthe image acquirer 110 via control signals and/or paths 130 to acquire,capture and/or otherwise obtain an image 107 at a selected resolutionand at a particular focal plane configured via the focal plane selector115.

As shown in FIG. 2, images 107 of the object 105 are captured forportions, regions, areas and/or tiles 210 and 211 of the object 105.That is, a complete two-dimensional (2D) image 107 of the object 105 ata particular focal plane includes a plurality of images 107 forrespective ones of a plurality of tiles 210 and 211. As shown in FIG. 2,the tiles 210 and 211 are arranged in strips 205-207 that traverse thewidth (or length) of the object 105. Each strip 205-207 is then dividedinto the regions, portions and/or tiles 210 and 211. As described below,the example acquisition controller 120 adaptively selects and/ordetermines the resolution at which the image 107 of a particular tile210, 211 is captured. To facilitate compression, reconstruction and/ordisplay of the entire 2D image from the images 107 of the constituenttiles 210 and 211, which may have been captured at differentresolutions, the strips 205-207 partially overlap with adjacent strips205-207, and the tiles 210 and 211 overlaps with adjacent tiles 210 and211.

A set of 2D images of the object 105 may be captured based on any numberand/or type(s) of sequence(s) and/or order(s). For example, startingwith a first focal plane, images 107 of the tiles 210 and 211 of thestrip 205 may be captured. When the strip 205 has been imaged, images107 of the tiles 210 and 211 of the next strip 206 may be captured. Theprocess continues until the entire first focal plane has been imaged,and then may be repeated for any additional focal planes. Additionallyor alternatively, for each tile 210, 211 of each strip 205-207 images107 may be captured for each focal plane before changing the tile 210,211 and/or strip 205-207.

Returning to FIG. 1, to determine at what resolution an image 107 is tobe captured, the example adaptive multi-focal plane image acquisitionand compression apparatus 100 of FIG. 1 includes a contrast detector140. For a presently considered image 107, the example contrast detector140 of FIG. 1 computes a value and/or metric 145 representative ofwhether the presently considered tile 210, 211 at the presentlyconsidered focal plane may be considered of interest by a pathologist.An example representative value and/or metric 145 is an estimate of thecontrast of the image 107 of the presently considered tile 210, 211. Theestimated contrasted may be computed using any number and/or type(s) ofmethod(s), algorithm(s) and/or logic. For example, a contrast metric 145may be computed as the variance of the intensity Y, of the pixels of acaptured image 107, which represents a measure of the local imagecontrast over the image 107. In general, high contrast values correspondto the presence of strong edges in an image and, thus, to potentialregions of interest for a pathologist. For a color image 107, theintensity value Y, of a single RGB pixel i can be estimated asY_(i)=0.299*R_(i)+0.587*G_(i)+0.114*B. Assuming that E(Y) is the averageof the intensity values Y_(i) over a presently considered tile 210, 211,the variance of the pixels of the image 107 (i.e., the contrast metric145) can be computed using the following mathematical expression:

$\begin{matrix}{{\sigma^{2} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( {Y_{i} - {E(Y)}} \right)^{2}}}},} & {{EQN}\mspace{14mu} (1)}\end{matrix}$

assuming the image has N pixels.

Another example contrast metric 145, which is an estimate of localvisual activity, is the Laplacian-Gaussian operator. TheLaplacian-Gaussian operator includes a Gaussian smoothing filterfollowed by a Laplacian differentiation filter. In general, large outputvalues of the Laplacian-Gaussian operator correspond to the presence ofstrong edges in an image and, thus, to potential regions of interest fora pathologist. Assuming GL(Y_(i)) is the output of a Laplacian-Gaussianfilter applied to the intensity value Y_(i), a contrast metric 145 forthe image can be expressed as:

$\begin{matrix}{\frac{1}{N}{\sum\limits_{i = 1}^{N}{{{GL}\left( Y_{i} \right)}.}}} & {{EQN}\mspace{14mu} (2)}\end{matrix}$

For a presently considered tile 210, 211 and focal plane, the exampleacquisition controller 120 of FIG. 1 controls the example image acquirer110 to capture a first image 107 of the presently considered tile 210,211 at a first or low resolution. The acquisition controller 120 directsthe example contrast detector 140 to compute a contrast metric 145 forthe first or low resolution image 107. The example acquisitioncontroller 120 compares the contrast metric 145 to a threshold todetermine whether to capture a second or higher resolution image 107 ofthe presently considered tile 210, 211 at the focal plane. When thecontrast metric 145 is greater than the threshold, the acquisitioncontroller 120 directs the image acquirer 110 to capture a second image107 of the presently considered tile 210, 211 at a second or high(er)resolution.

In some examples, the second or high(er) resolution is the highestresolution of the image acquirer 110 and the first or low(er) resolutionis a resolution corresponding to a thumbnail resolution. Additionally oralternatively, the example acquisition controller 120 directs the imageacquirer 110 to take successive images 107 of the presently consideredtile 210, 211 at increasing resolutions until the contrast metric 145computed by the contrast detector 145 for each successive image 107 nolonger exceeds the threshold. In some example, the contrast metric 145is compared to different thresholds depending on the resolution of theimage 107.

As shown in FIGS. 3 and 4, the resolution at which images 107 fordifferent tiles 210 and 211 and different focal planes may vary throughthe object 105. FIG. 3 depicts an object 105 having a feature 305 thatoccurs at different focal planes (three of which are depicted atreference numerals 310, 311 and 312) within the object 105. For example,at a first tile 315 images 107 of the object 205 are captured at ahigh(er) resolution at a focal plane 310 and at a low(er) resolution ata focal plane 311. In some instances, a tile 316 may be captured with ahigh(er) resolution at two adjacent focal planes 311 and 312 when thefeature 305 occurs between and/or substantially near two focal planes311 and 312 within the tile 316.

FIG. 4 depicts an object 105 having two features 405 and 406 that are atpotentially different focal planes (two of which are depicted atreference numerals 410 and 411) within the object 105. For example, atissue sample having more than one layer of cell structures may havemore than one feature of interest. Accordingly, a tile 415 may becaptured with a high or higher resolution at two adjacent and/ornon-adjacent focal planes 410 and 411. Based on at least FIGS. 3 and 4,it should be clear that any tile 210, 211 might be captured at a higheror high resolution for any number of adjacent and/or non-adjacent focalplanes depending on the feature(s) present in the object 105 within thetile 210, 211.

FIG. 5 illustrates an example logical multi-scale depiction 500 of themulti-resolution 2D image captured for a particular focal plane. Asshown in FIG. 5, the 2D image comprises images 107 of different tiles210 and 211 captured at different resolutions. For example, an image 107captured for a tile 505 was captured at the highest resolution of theimage acquirer 110, while an image 107 capture for another tile 510 wascaptured at a lower resolution. Thus, the resolution at which an image107 is captured determines where in the multi-scale representation 500each captured image 107 and/or data representative of the captured image107 logically corresponds. In the illustrated example of FIG. 5,adjacent resolutions differ by a factor of two.

A representation of any portion of a captured multi-resolution 2D imagefor any particular resolution can be generated by appropriateinterpolation and/or decimation of the captured tile images 107. Forexample, a 1:4 scale image of the 2D image in the vicinity of the tile505 can be generated by decimating the image 107 corresponding to thetile 505 by a factor of four.

While the example multi-scale depiction 500 of FIG. 5 may be used toactually store a representation of a multi-scale 2D image, an exampledata structure 600 that may be used to more effectively store amulti-scale 2D image is described below in connection with FIG. 6. Ingeneral, the example data structure 600 of FIG. 6 is formed by“flattening” the example depiction 500 and removing portions that do notcontain image data. It should be understood that the example depiction500 of FIG. 5 and the example data structure 600 of FIG. 6 may representessentially equivalent representations of a multi-scale 2D image.

Returning to FIG. 1, to compress the images 107 captured by the exampleimage acquirer 110 and the example acquisition controller 120, theexample adaptive multi-focal plane image acquisition and compressionapparatus 100 of FIG. 1 includes a compression module 150. For thehighest resolution image 107 captured for a particular tile 210, 211,the example image compression module 150 applies a wavelet transform togenerate one or more wavelet coefficients that represent that image 107.The example compression module 150 stores the computed waveletcoefficients in an image database 155. The computed wavelet coefficientscan be stored in the image database 155 using the example structure(s)of FIGS. 5 and/or 6. The example image database 155 of FIG. 1 may beimplemented using any number and/or type(s) of memory(-ies), memorydevice(s) and/or storage device(s) such as a hard disk drive, a compactdisc (CD), a digital versatile disc (DVD), a floppy drive, etc. Thehigh(-est) frequency wavelet coefficients are associated with thepresently considered tile 210, 211 at the highest resolution at whichthe tile 210, 211 was imaged, while the low frequency waveletcoefficients are associated with the presently considered tile 210, 211at the next lower resolution, as depicted in FIG. 7.

FIG. 7 depicts a “virtual” wavelet transform that represents the entire2D image associated with a particular focal plane. As shown in FIG. 7,each tile 210 of the focal plane has associated wavelet coefficients 705computed based on the image 107 captured for that tile 210 at theresolution selected by the example acquisition controller 120. Thewavelet coefficients 705 can be regarded as multi-scale edge detectors,where the absolute value of a wavelet coefficient 705 corresponds to thelocal strength of that edge, that is, how likely the edge and, thus, thetile 210 may be of interest to a pathologist. In the illustrated exampleof FIG. 7, low-frequency edges are depicted in the upper left-handcorner of the wavelet coefficients 705, with progressivelyhigher-frequency edges occur downward and/or rightward in the waveletcoefficients 705.

Taken collectively, the wavelet coefficients 705 for all of the tiles210, 211 of the focal plane can be used to generate a representation ofthe image 105 at that focal plane and at any selected resolution.Example methods and apparatus that may be used to display, represent,transmit and/or store a set of 2D multi-resolution images as athree-dimensional (3D) image are described in U.S. Pat. No. 7,376,279,entitled “Three-dimensional Image Streaming System and Method forMedical Images,” issued May 20, 2008, and which is hereby incorporatedby reference in its entirety.

Returning to FIG. 1, to further reduce the amount of data needed tostore the representation 155 of the object 105, the example imagecompression module 140 of FIG. 1 may further process the computedwavelet coefficients to reduce redundancy and/or to reduce the amount ofdata needed to store and/or represent the wavelet coefficients. Usingany number and/or type(s) of algorithm(s), method(s) and/or logic, theimage compression module 140 may quantize and/or entropy encode thecomputed wavelet coefficients according to their tree-structure using,for example, a so-called “zero-tree” compression algorithm. In someexamples, local groups of wavelet coefficients at given tiles and/orfocal planes are compressed into different data blocks. By groupingwavelet coefficients in different data blocks, only a portion of thecompressed image 155 needs to be extracted to begin reconstructing animage of the original object 105. Such groupings of wavelet coefficientsfacilitate the rendering an image of only a particularregion-of-interest of the object 105 at a particular focal plane andresolution, and/or facilitate the progressive reconstruction of an imageof the object 105.

While the examples described herein utilize wavelet coefficients torepresent a compressed version of the captured images 107 of the object105, any number and/or type(s) of additional and/or alternativecompression method(s), technique(s), and/or algorithm(s) may be applied.For example, each of the captured images 107 could be interpolated, asnecessary, to a common resolution. The interpolated images 107 couldthen be compressed in accordance with, for example, the jointphotographic experts group (JPEG) and/or JPEG2000 standards.

While an example adaptive multi-focal plane image acquisition andcompression apparatus 100 has been illustrated in FIG. 1, one or more ofthe interfaces, data structures, elements, processes and/or devicesillustrated in FIG. 1 may be combined, divided, re-arranged, omitted,eliminated and/or implemented in any other way. Further, the exampleimage acquirer 110, the example focal plane selector 115, the exampleacquisition controller 120, the example contrast detector 140, theexample image compression module 150 and/or, more generally, the exampleadaptive multi-focal plane image acquisition and compression apparatus100 of FIG. 1 may be implemented by hardware, software, firmware and/orany combination of hardware, software and/or firmware. Thus, forexample, any of the example image acquirer 110, the example focal planeselector 115, the example acquisition controller 120, the examplecontrast detector 140, the example image compression module 150 and/or,more generally, the example adaptive multi-focal plane image acquisitionand compression apparatus 100 may be implemented by one or morecircuit(s), programmable processor(s), application-specific integratedcircuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)),field-programmable logic device(s) (FPLD(s)), and/or field-programmablegate array(s) (FPGA(s)), etc. When any of the appended claims are readto cover a purely software and/or firmware implementation, at least oneof the example image acquirer 110, the example focal plane selector 115,the example acquisition controller 120, the example contrast detector140, the example image compression module 150 and/or, more generally,the example adaptive multi-focal plane image acquisition and compressionapparatus 100 are hereby expressly defined to include a tangiblecomputer-readable medium such as a memory, a DVD, a CD, a hard disk, afloppy disk, etc. storing the firmware and/or software. Further still,the example adaptive multi-focal plane image acquisition and compressionapparatus 100 may include interfaces, data structures, elements,processes and/or devices instead of, or in addition to, thoseillustrated in FIG. 1 and/or may include more than one of any or all ofthe illustrated interfaces, data structures, elements, processes and/ordevices.

FIG. 6 illustrates an example data structure 600 that may be used toimplement the example image database 155 of FIG. 1. The example datastructure 600 of FIG. 6 includes a plurality of entries 605 forrespective combinations of tiles 210, 211 and focal planes. To identifya tile 210, 211, each of the example entries 605 of FIG. 6 includes atile field 610. Each of the example tile fields 610 of FIG. 6 includesone or more numbers and/or identifiers that represent a particular tile210, 211 at a particular focal plane 615 and at a particular resolution620.

To identify a focal plane, each of the example entries 605 of FIG. 6includes a focal plane field 615. Each of the example focal plane fields615 of FIG. 6 contains a number and/or identifier that represents afocal plane at which an image 107 was acquired.

To identify an image resolution, each of the example entries 605 of FIG.6 includes a resolution field 620. Each of the example resolution fields620 of FIG. 6 includes a number and/or identifier that represents theresolution at which an image 107 of the tile 610 was captured.

To identify a position of the tile 610, each of the example entries 605of FIG. 6 includes a position field 625. Each of the example positionfields 625 of FIG. 6 includes two values and/or indices thatrespectively represent an x-coordinate and a y-coordinate.

To specify a quality layer, each of the example entries 605 of FIG. 6includes a quality field 630. Each of the example quality fields 630 ofFIG. 6 includes a value that may be used to facilitate, for example,progressive rendering.

To store wavelet coefficients, each of the example entries 605 of FIG. 6includes a wavelet coefficients field 635. Each of the example waveletcoefficients field 635 of FIG. 6 stores one or more wavelet coefficientscomputed for the tile 610. In some examples, 3D wavelets may be used toexploit the correlation between focal planes and, thus, the wavelets 635represent differences between focal planes. In such examples, the valuecontained in the plane field 615 does not correspond to a specific focalplane, but rather with a difference of focal planes for the tile 610.

While an example data structure 600 that may be used to implement theexample image database 155 has been illustrated in FIG. 6, one or moreof the entries and/or fields may be combined, divided, re-arranged,omitted, eliminated and/or implemented in any other way. For example,the quality field 630 may be omitted in some examples. Moreover, theexample data structure 600 of FIG. 6 may include fields instead of, orin addition to, those illustrated in FIG. 6 and/or may include more thanone of any or all of the illustrated fields.

FIGS. 8 and 9 illustrate example processes that may be carried out toimplement the example adaptive multi-focal plane image acquisition andcompression apparatus 100 of FIG. 1. A processor, a controller and/orany other suitable processing device may be used and/or programmed tocarry out the example processes of FIGS. 8 and/or 9. For example, theexample processes of FIGS. 8 and/or 9 may be embodied in codedinstructions stored on a tangible computer-readable medium such as aflash memory, a CD, a DVD, a floppy disk, a read-only memory (ROM), arandom-access memory (RAM), a programmable ROM (PROM), anelectronically-programmable ROM (EPROM), and/or anelectronically-erasable PROM (EEPROM), an optical storage disk, anoptical storage device, magnetic storage disk, a magnetic storagedevice, and/or any other medium which can be used to carry or storeprogram code and/or instructions in the form of machine-readableinstructions or data structures, and which can be accessed by aprocessor, a general purpose or special purpose computer or othermachine with a processor (e.g., the example processor platform P100discussed below in connection with FIG. 10). Combinations of the aboveare also included within the scope of computer-readable media.Machine-readable instructions comprise, for example, instructions anddata that cause a processor, a general-purpose computer, special purposecomputer, or a special-purpose processing machine to perform one or moreparticular processes. Alternatively, some or all of the exampleprocesses of FIGS. 8 and/or 9 may be implemented using anycombination(s) of ASIC(s), PLD(s), FPLD(s), FPGA(s), discrete logic,hardware, firmware, etc. Also, some or all of the example processes ofFIGS. 8 and/or 9 may be implemented manually or as any combination ofany of the foregoing techniques, for example, any combination offirmware, software, discrete logic and/or hardware. Further, many othermethods of implementing the example operations of FIGS. 8 and/or 9 maybe employed. For example, the order of execution of the blocks may bechanged, and/or one or more of the blocks described may be changed,eliminated, sub-divided, or combined. Additionally, any or all of theexample processes of FIGS. 8 and/or 9 may be carried out sequentiallyand/or carried out in parallel by, for example, separate processingthreads, processors, devices, discrete logic, circuits, etc.

The example process of FIG. 8 begins with adaptively acquiring an image107 of presently considered tile 210, 211 at presently considered focalplane by, for example, carrying out the example process of FIG. 9 (block805). If there are more tiles 210, 211 on the presently considered focalplane that have not been imaged (block 815), the next tile 210, 211 isselected (block 820), and control returns to block 805 to adaptiveacquire an image for the selected next tile 210, 211.

If all the tiles 210, 211 of the presently considered focal plane havebeen imaged (block 810), and an additional focal plane is to be imaged(block 815), the acquisition controller 120 controls the example focalplane selector 115 to focus the example image acquirer 110 on a nextfocal plane (block 825), and controls returns to block 805 to adaptiveimage the selected next focal plane.

When all focal planes have been imaged (block 825), control exits fromthe example process of FIG. 8.

The example process of FIG. 9 begins with the example acquisitioncontroller 120 selecting an initial resolution (block 905). The exampleimage acquirer 110 captures an image 107 of the presently consideredtile 210, 211 at the selected resolution (block 910). The examplecontrast detector 140 computes the contrast metric 145 for the capturedimage 107 by carrying out, for example, either of the examplemathematical expressions of EQN (1) and EQN (2) (block 915).

The acquisition controller 120 compares the contrast metric 145 to athreshold to determine whether to capture a second image 107 of thepresently considered tile 210, 211 (block 920). If the contrast metric145 is greater than the threshold (block 920), the acquisitioncontroller 120 changes the resolution of the image acquirer 110 (block925), and control returns to block 910 to capture another image 107 ofthe presently considered tile 210, 211.

When the contrast 145 is not greater than the threshold (block 920), theexample image compression module 150 computes wavelet coefficients forthe last image 107 captured for the presently considered tile 210, 211(block 930). The image compression module 150 quantizes and/or encodesthe computed wavelet coefficients (block 935) and stores them in theexample image database 155 (block 940). Control then exits from theexample process of FIG. 9 to, for example, the example process of FIG. 8at block 810.

FIG. 10 is a schematic diagram of an example processor platform P100that may be used and/or programmed to implement the example adaptivemulti-focal plane image acquisition and compression apparatus 100 ofFIG. 1. For example, the processor platform P100 can be implemented byone or more general-purpose processors, processor cores,microcontrollers, etc.

The processor platform P100 of the example of FIG. 10 includes at leastone general-purpose programmable processor P105. The processor P105executes coded instructions P110 and/or P112 present in main memory ofthe processor P105 (e.g., within a RAM P115 and/or a ROM P120). Theprocessor P105 may be any type of processing unit, such as a processorcore, a processor and/or a microcontroller. The processor P105 mayexecute, among other things, the example process of FIGS. 8 and/or 9 toimplement the example adaptive multi-focal plane image acquisition andcompression methods and apparatus described herein.

The processor P105 is in communication with the main memory (including aROM P120 and/or the RAM P115) via a bus P125. The RAM P115 may beimplemented by dynamic random access memory (DRAM), synchronous dynamicrandom access memory (SDRAM), and/or any other type of RAM device, andROM may be implemented by flash memory and/or any other desired type ofmemory device. Access to the memory P115 and the memory P120 may becontrolled by a memory controller (not shown). The example memory P115may be used to implement the example image database 155 of FIG. 1.

The processor platform P100 also includes an interface circuit P130. Theinterface circuit P130 may be implemented by any type of interfacestandard, such as an external memory interface, serial port,general-purpose input/output, etc. One or more input devices P135 andone or more output devices P140 are connected to the interface circuitP130. The input devices P135 may be used to, for example, receive images107 from the example image acquirer 110. The example output devices P140may be used to, for example, control the example image acquirer 110and/or the example focal plane selector 115.

Generally, computer-executable instructions include routines, programs,objects, components, data structures, etc., that perform particulartasks or implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of program code for executing the processes to implement theexample methods and systems disclosed herein. The particular sequence ofsuch executable instructions and/or associated data structures representexamples of corresponding acts for implementing the examples describedherein.

The example methods and apparatus described herein may be practiced in anetworked environment using logical connections to one or more remotecomputers having processors. Logical connections may include a localarea network (LAN) and a wide area network (WAN) that are presented hereby way of example and not limitation. Such networking environments arecommonplace in office-wide or enterprise-wide computer networks,intranets and the Internet and may use a wide variety of differentcommunication protocols. Such network computing environments mayencompass many types of computer system configurations, includingpersonal computers, hand-held devices, multi-processor systems,microprocessor-based or programmable consumer electronics, network PCs,minicomputers, mainframe computers, and the like. The example methodsand apparatus described herein may, additionally or alternatively, bepracticed in distributed computing environments where tasks areperformed by local and remote processing devices that are linked (eitherby hardwired links, wireless links, or by a combination of hardwired orwireless links) through a communications network. In a distributedcomputing environment, program modules may be located in both local andremote memory storage devices.

Although certain example methods, apparatus and articles of manufacturehave been described herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe appended claims either literally or under the doctrine ofequivalents.

1. A method comprising: capturing a first set of images of an object ata first focal plane and at a first resolution when a comparisonindicates that a contrast metric associated with the object is less thanor equal to a threshold; capturing a second set of images of the objectat the first focal plane and at a second resolution higher than thefirst resolution when the comparison indicates that the contrast metricassociated with the object is greater than a threshold; for portions ofthe object at which the contrast metric caused the first images to becaptured at the first resolution, computing first wavelet coefficientsof a first frequency; for portions of the object at which the contrastmetric caused the second images to be captured at the second resolution,computing second wavelet coefficients of a second frequency differentthan the first frequency; compressing the second wavelet coefficients ofthe second frequency; and storing the compressed second wavelets in arepresentation of the object.
 2. A method as defined in claim 1, whereincompressing the second wavelet coefficients comprises compressing afirst local group of the second wavelet coefficients into a first datablock and a second local group of the second wavelet coefficients into asecond data block different than the first data block.
 3. A method asdefined in claim 2, wherein the first data block is able to be extractedfor rendering independently from the second data block.
 4. A method asdefined in claim 1, wherein the second frequency is greater than thefirst frequency.
 5. A method as defined in claim 1, wherein thethreshold is set to identify regions of interest of in the object viathe comparison involving the contrast metric.
 6. A method as defined inclaim 1, wherein computing the wavelet coefficients comprises assigningan absolute value to respective ones of the wavelet coefficients thatrepresents a likelihood that a corresponding region of the object is ofinterest to a user.
 7. A method as defined in claim 6, wherein the useris a pathologist and the object is a medical image.
 8. An apparatuscomprising: an image acquirer to: capture a first set of images of anobject at a first focal plane and at a first resolution when acomparison indicates that a contrast metric associated with the objectis less than or equal to a threshold; and capture a second set of imagesof the object at the first focal plane and at a second resolution higherthan the first resolution when the comparison indicates that thecontrast metric associated with the object is greater than a threshold;and an image compression module to: for portions of the object at whichthe contrast metric caused the first images to be captured at the firstresolution, compute first wavelet coefficients of a first frequency; forportions of the object at which the contrast metric caused the secondimages to be captured at the second resolution, compute second waveletcoefficients of a second frequency different than the first frequency;compress the second wavelet coefficients of the second frequency; andstore the compressed second wavelets in a representation of the object.9. An apparatus as defined in claim 8, wherein compressing the secondwavelet coefficients comprises compressing a first local group of thesecond wavelet coefficients into a first data block and a second localgroup of the second wavelet coefficients into a second data blockdifferent than the first data block.
 10. An apparatus as defined inclaim 9, wherein the first data block is able to be extracted forrendering independently from the second data block.
 11. An apparatus asdefined in claim 8, wherein the second frequency is greater than thefirst frequency.
 12. An apparatus as defined in claim 8, wherein thethreshold is set to identify regions of interest of in the object viathe comparison involving the contrast metric.
 13. An apparatus asdefined in claim 8, wherein computing the wavelet coefficients comprisesassigning an absolute value to respective ones of the waveletcoefficients that represents a likelihood that a corresponding region ofthe object is of interest to a user.
 14. An apparatus as defined inclaim 13, wherein the user is a pathologist and the object is a medicalimage.
 15. A tangible machine-readable storage medium comprisinginstructions that, when executed, cause a machine to at least: capture afirst set of images of an object at a first focal plane and at a firstresolution when a comparison indicates that a contrast metric associatedwith the object is less than or equal to a threshold; capture a secondset of images of the object at the first focal plane and at a secondresolution higher than the first resolution when the comparisonindicates that the contrast metric associated with the object is greaterthan a threshold; and for portions of the object at which the contrastmetric caused the first images to be captured at the first resolution,compute first wavelet coefficients of a first frequency; for portions ofthe object at which the contrast metric caused the second images to becaptured at the second resolution, compute second wavelet coefficientsof a second frequency different than the first frequency; compress thesecond wavelet coefficients of the second frequency; and store thecompressed second wavelets in a representation of the object.
 16. Astorage medium as defined in claim 15, wherein compressing the secondwavelet coefficients comprises compressing a first local group of thesecond wavelet coefficients into a first data block and a second localgroup of the second wavelet coefficients into a second data blockdifferent than the first data block.
 17. A storage medium as defined inclaim 16, wherein the first data block is able to be extracted forrendering independently from the second data block.
 18. A storage mediumas defined in claim 15, wherein the second frequency is greater than thefirst frequency.
 19. A storage medium as defined in claim 15, whereinthe threshold is set to identify regions of interest of in the objectvia the comparison involving the contrast metric.
 20. A storage mediumas defined in claim 15, wherein computing the wavelet coefficientscomprises assigning an absolute value to respective ones of the waveletcoefficients that represents a likelihood that a corresponding region ofthe object is of interest to a user.